Rlhf Feedback Loop
Capture thumbs-up/down on agent outputs, block bad actions, and export DPO-ready preference data for your own agent tuning.
Overview
RLHF Feedback Loop is a MCP server for the Build phase that captures agent feedback, blocks mistakes, and exports DPO training data.
What is this MCP server?
- RLHF-style feedback capture tied to agent runs
- Mistake blocking aligned with gateway-style guardrails
- DPO dataset export for preference fine-tuning pipelines
- npm package rlhf-feedback-loop v0.6.7 with stdio MCP
- Complements mcp-memory-gateway in the same author stack
- Package version 0.6.7 on npm as rlhf-feedback-loop
- stdio MCP transport in server manifest
- Repository: IgorGanapolsky/rlhf-feedback-loop on GitHub
What problem does it solve?
You correct your agent constantly but those preferences never leave the chat, so the next session makes the same errors.
Who is it for?
Indie builders running MCP agents who plan to fine-tune or evaluate models with preference data from real coding sessions.
Skip if: Builders who only need one-off codegen without any feedback dataset or tuning pipeline.
What do I get? / Deliverables
Feedback becomes structured records you can block on and export as DPO pairs for fine-tuning or evaluation.
- Structured feedback records from agent sessions
- Exported DPO-oriented preference datasets
- Mistake blocks coordinated with feedback capture
Recommended MCP Servers
Journey fit
Preference capture and DPO export are part of constructing reliable agent workflows during Build, before you treat the agent as production-complete. The server is tooling around agent behavior and training data—not end-user product features.
How it compares
RLHF capture and DPO export MCP server, not a hosted labeling marketplace or generic analytics dashboard.
Common Questions / FAQ
Who is RLHF Feedback Loop for?
It is for developers building agent workflows who want to log preferences, stop repeat mistakes, and export DPO-ready data.
When should I use RLHF Feedback Loop?
Use it while iterating on agent behavior during Build when you are ready to formalize feedback beyond informal chat corrections.
How do I add RLHF Feedback Loop to my agent?
Install rlhf-feedback-loop from npm, add it as a stdio MCP server in your agent client, and connect your feedback storage or export path per the GitHub README.